Physics-informed Ground Reaction Dynamics from Human Motion Capture

TL;DR

This paper introduces a physics-informed method to estimate human ground reaction forces directly from motion capture data, eliminating the need for force plates and improving accuracy in human motion analysis.

Contribution

The authors develop a novel physics-based approach that estimates ground reaction forces from motion capture data using Euler's integration and PD algorithms, bypassing laboratory force plates.

Findings

Outperforms baseline in ground reaction force estimation accuracy

Achieves more precise root trajectory simulation

Demonstrates robustness across diverse motion data

Abstract

Body dynamics are crucial information for the analysis of human motions in important research fields, ranging from biomechanics, sports science to computer vision and graphics. Modern approaches collect the body dynamics, external reactive force specifically, via force plates, synchronizing with human motion capture data, and learn to estimate the dynamics from a black-box deep learning model. Being specialized devices, force plates can only be installed in laboratory setups, imposing a significant limitation on the learning of human dynamics. To this end, we propose a novel method for estimating human ground reaction dynamics directly from the more reliable motion capture data with physics laws and computational simulation as constrains. We introduce a highly accurate and robust method for computing ground reaction forces from motion capture data using Euler's integration scheme and PD algorithm. The physics-based reactive forces are used to inform the learning model about the physics-informed motion dynamics thus improving the estimation accuracy. The proposed approach was tested on the GroundLink dataset, outperforming the baseline model on: 1) the ground reaction force estimation accuracy compared to the force plates measurement; and 2) our simulated root trajectory precision. The implementation code is available at https://github.com/cuongle1206/Phys-GRD